基于稀疏表示的双基地MIMO雷达多目标定位及幅相误差估计
收稿日期: 2012-07-04
修回日期: 2012-11-09
网络出版日期: 2013-01-05
基金资助
国家自然科学基金(60702015)
Localization and Estimation of Gain-phase Error for Bistatic MIMO Radar Based on Sparse Representation
Received date: 2012-07-04
Revised date: 2012-11-09
Online published: 2013-01-05
Supported by
National Natural Science Foundation of China (60702015)
基于稀疏表示理论,提出一种新的双基地多输入多输出(MIMO)雷达收发角度及幅相误差估计算法。利用接收数据,分别构造发射和接收协方差矩阵,并以列向量化后的发射和接收协方差矩阵为量测信号建立2个一维稀疏线性模型,构造模型求解的 L2-L1 混合范数优化目标函数,通过交替迭代寻优获得目标角度估计和幅相误差估计,最后给出了本文算法的收敛性分析。与现有算法相比,该算法充分利用了目标发射和接收空域的稀疏特性,且能够通过对噪声功率的预估计来抑制噪声。仿真结果表明:在低信噪比(SNR)条件下,本文算法仍能够得到较好的估计精度,且对幅相误差具有一定的稳健性。
郑志东 , 张剑云 , 宋靖 , 徐旭宇 . 基于稀疏表示的双基地MIMO雷达多目标定位及幅相误差估计[J]. 航空学报, 2013 , 34(6) : 1379 -1388 . DOI: 10.7527/S1000-6893.2013.0238
A new algorithm is presented for the joint estimation of angle and gain-phase error of a bistatic multiple-input multiple-output (MIMO) radar based on sparse representation. The transmitting and receiving covariance matrices are constructed by using the received data. Two one-dimensional sparse linear models are obtained by performing the vectorization operation on the transmitting and receiving covariance matrices. Then the mixed L2-L1 norm cost functions are constructed, in which the solution is derived by utilizing the alternating minimization technique. Furthermore, the coverage analysis of the iterative algorithm is provided. Compared with the existing algorithms, the proposed method fully utilizes the sparse characteristic of the spatial field of a target, and the noise can be suppressed by pre-estimating the noise power. The simulation results show that the proposed method can achieve good estimation performance even under low signal to noise ratio (SNR) and is robust against the variation of gain-phase errors.
[1] Fisher E, Haimovich A, Blum R S, et al. Spatial diversity in radar-models and detection performance. IEEE Transactions on Signal Processing, 2006, 54(3): 823-838.
[2] Haimovich A M, Blum R S, Lenard J, et al. MIMO radar with widely separated antennas. IEEE Signal Processing Magazine, 2008, 25(1): 116-129.
[3] Chao S Y, Chen B X, Dai F Z. MIMO radar detection in nonhomogeneous clutter. Acta Electronica Sinica, 2011, 39(3): 627-631. (in Chinese) 晁淑媛, 陈伯孝, 戴奉周. 非均匀杂波MIMO雷达检测. 电子学报, 2011, 39(3): 627-631.
[4] Li J, Stoica P. MIMO radar with collocated antennas. IEEE Signal Processing Magazine, 2007, 24(5): 106-114.
[5] Lv H, Feng D Z, He J, et al. Two-stage reduced-dimension clutter suppression method for airborne MIMO radar. Journal of Electronic & Information Technology, 2011, 33(4): 805-809. (in Chinese) 吕辉, 冯大政, 和洁, 等. 机载MIMO雷达两级降维杂波抑制方法. 电子与信息学报, 2011, 33(4): 805-809.
[6] Chen D F, Chen B X, Qin G D. Angle estimation using ESPRIT in MIMO radar. IEE Electronics Letters, 2008, 44(12): 770-771.
[7] Chen J L, Gu H, Su W M. Angle estimation using ESPRIT without pairing in MIMO radar. IEE Electronics Letters, 2008, 44(24): 1422-1423.
[8] Cheng Y B, Gu H, Su W M. A new method for fast multi-target localization in bistatic MIMO radar. Journal of Electronic & Information Technology, 2012, 34(2): 312-317. (in Chinese) 程院兵, 顾红, 苏卫民. 一种新的双基地MIMO雷达快速多目标定位算法. 电子与信息学报, 2012, 34(2): 312-317.
[9] Li J F, Zhang X F, Wang F. Quaternion Root-MUSIC algorithm for angle estimation in bistatic MIMO radar. Journal of Electronic & Information Technology, 2012, 34(2): 300-304. (in Chinese) 李建峰, 张小飞, 汪飞. 基于四元数的Root-MUSIC的双基地MIMO雷达中角度估计算法. 电子与信息学报, 2012, 34(2): 300-304.
[10] Hyder M M, Mahata K. A joint sparse signal representation perspective for target detection using bistatic MIMO radar system. International Conference on Digital Signal Processing, 2011: 1-5.
[11] Swindlehurst A, Karlath T. A performance analysis of subspace-based methods in the presence of model error: Part I-the MUSIC algorithm. IEEE Transactions on Signal Processing, 1992, 40(7): 1758-1774.
[12] Liu X L, Liao G S. Multitarget localization and estimation of gain-phase error for bistatic MIMO radar. Acta Electronica Sinica, 2011, 39(3): 596-601. (in Chinese) 刘晓莉, 廖桂生. 双基地MIMO雷达多目标定位及幅相误差估计. 电子学报, 2011, 39(3): 596-601.
[13] Ma W K, Han H T, Yung C C. DOD estimation of quasi-stationary signals with less sensors than sources and unknown spatial noise covariance: A Khatri-Rao subspace approach. IEEE Transactions on Signal Processing, 2010, 58(4): 2169-2180.
[14] Yin J, Chen T. Direction-of-arrival estimation using a sparse representation of array covariance vectors. IEEE Transactions on Signal Processing, 2011, 59(9): 4489-4493.
[15] Batu O, Cetin M. Parameter selection in sparsity-driven SAR imaging. IEEE Transactions on Aerospace and Electronic Systems, 2011, 47(4): 3040-3050.
[16] Zhang X D. Matrix analysis and applications. Beijing: Tsinghua Press, 2004: 104-110. (in Chinese) 张贤达. 矩阵分析与应用. 北京: 清华大学出版社, 2004: 104-110.
[17] Nocedal J, Wright S. Numerical optimization. New York: Springer, 1999: 35-65.
[18] Bill M, Radich K M. Single-snapshot DOA estimation and source number detection. IEEE Signal Processing Letters, 1997, 4(4): 109-111.
[19] Liu Y, Wu M Y, Wu S J. Fast OMP algorithm for 2D angle estimation in MIMO radar. Electronics Letters, 2010, 46(6): 444-445.
[20] Daubechies I, Devore R, Fornasier M. Iteratively-reweighted least squares minimization for sparse recovery. Applied Mathematics, 2009, 63(1): 1-38.
[21] Dmitry M, Mujdat C, Alan S W. A sparse signal reconstruction perspective for source localization with sensor arrays. IEEE Transactions on Signal Processing, 2005, 53(8): 3010-3023.
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